Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations20640
Missing cells207
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory80.0 B

Variable types

Numeric9
Categorical1

Alerts

households is highly overall correlated with population and 2 other fieldsHigh correlation
latitude is highly overall correlated with longitudeHigh correlation
longitude is highly overall correlated with latitudeHigh correlation
median_house_value is highly overall correlated with median_incomeHigh correlation
median_income is highly overall correlated with median_house_valueHigh correlation
population is highly overall correlated with households and 2 other fieldsHigh correlation
total_bedrooms is highly overall correlated with households and 2 other fieldsHigh correlation
total_rooms is highly overall correlated with households and 2 other fieldsHigh correlation
total_bedrooms has 207 (1.0%) missing valuesMissing

Reproduction

Analysis started2025-11-29 15:12:05.650021
Analysis finished2025-11-29 15:12:17.104490
Duration11.45 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

longitude
Real number (ℝ)

High correlation 

Distinct844
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-119.5697
Minimum-124.35
Maximum-114.31
Zeros0
Zeros (%)0.0%
Negative20640
Negative (%)100.0%
Memory size161.4 KiB
2025-11-29T20:42:17.181259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-124.35
5-th percentile-122.47
Q1-121.8
median-118.49
Q3-118.01
95-th percentile-117.08
Maximum-114.31
Range10.04
Interquartile range (IQR)3.79

Descriptive statistics

Standard deviation2.0035317
Coefficient of variation (CV)-0.016756182
Kurtosis-1.3301524
Mean-119.5697
Median Absolute Deviation (MAD)1.28
Skewness-0.29780121
Sum-2467918.7
Variance4.0141394
MonotonicityNot monotonic
2025-11-29T20:42:17.401896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-118.31162
 
0.8%
-118.3160
 
0.8%
-118.29148
 
0.7%
-118.27144
 
0.7%
-118.32142
 
0.7%
-118.28141
 
0.7%
-118.35140
 
0.7%
-118.36138
 
0.7%
-118.19135
 
0.7%
-118.37128
 
0.6%
Other values (834)19202
93.0%
ValueCountFrequency (%)
-124.351
 
< 0.1%
-124.32
 
< 0.1%
-124.271
 
< 0.1%
-124.261
 
< 0.1%
-124.251
 
< 0.1%
-124.233
< 0.1%
-124.221
 
< 0.1%
-124.213
< 0.1%
-124.194
< 0.1%
-124.186
< 0.1%
ValueCountFrequency (%)
-114.311
 
< 0.1%
-114.471
 
< 0.1%
-114.491
 
< 0.1%
-114.551
 
< 0.1%
-114.561
 
< 0.1%
-114.573
< 0.1%
-114.582
< 0.1%
-114.592
< 0.1%
-114.63
< 0.1%
-114.613
< 0.1%

latitude
Real number (ℝ)

High correlation 

Distinct862
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.631861
Minimum32.54
Maximum41.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2025-11-29T20:42:17.600023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum32.54
5-th percentile32.82
Q133.93
median34.26
Q337.71
95-th percentile38.96
Maximum41.95
Range9.41
Interquartile range (IQR)3.78

Descriptive statistics

Standard deviation2.1359524
Coefficient of variation (CV)0.059945013
Kurtosis-1.1177598
Mean35.631861
Median Absolute Deviation (MAD)1.23
Skewness0.465953
Sum735441.62
Variance4.5622926
MonotonicityNot monotonic
2025-11-29T20:42:17.679121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.06244
 
1.2%
34.05236
 
1.1%
34.08234
 
1.1%
34.07231
 
1.1%
34.04221
 
1.1%
34.09212
 
1.0%
34.02208
 
1.0%
34.1203
 
1.0%
34.03193
 
0.9%
33.93181
 
0.9%
Other values (852)18477
89.5%
ValueCountFrequency (%)
32.541
 
< 0.1%
32.553
 
< 0.1%
32.5610
 
< 0.1%
32.5718
0.1%
32.5826
0.1%
32.5911
0.1%
32.69
 
< 0.1%
32.6114
0.1%
32.6213
0.1%
32.6318
0.1%
ValueCountFrequency (%)
41.952
< 0.1%
41.921
 
< 0.1%
41.881
 
< 0.1%
41.863
< 0.1%
41.841
 
< 0.1%
41.821
 
< 0.1%
41.812
< 0.1%
41.83
< 0.1%
41.791
 
< 0.1%
41.783
< 0.1%

housing_median_age
Real number (ℝ)

Distinct52
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.639486
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2025-11-29T20:42:17.758346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q118
median29
Q337
95-th percentile52
Maximum52
Range51
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.585558
Coefficient of variation (CV)0.43944774
Kurtosis-0.80062885
Mean28.639486
Median Absolute Deviation (MAD)10
Skewness0.060330638
Sum591119
Variance158.39626
MonotonicityNot monotonic
2025-11-29T20:42:17.846385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521273
 
6.2%
36862
 
4.2%
35824
 
4.0%
16771
 
3.7%
17698
 
3.4%
34689
 
3.3%
26619
 
3.0%
33615
 
3.0%
18570
 
2.8%
25566
 
2.7%
Other values (42)13153
63.7%
ValueCountFrequency (%)
14
 
< 0.1%
258
 
0.3%
362
 
0.3%
4191
0.9%
5244
1.2%
6160
0.8%
7175
0.8%
8206
1.0%
9205
1.0%
10264
1.3%
ValueCountFrequency (%)
521273
6.2%
5148
 
0.2%
50136
 
0.7%
49134
 
0.6%
48177
 
0.9%
47198
 
1.0%
46245
 
1.2%
45294
 
1.4%
44356
 
1.7%
43353
 
1.7%

total_rooms
Real number (ℝ)

High correlation 

Distinct5926
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2635.7631
Minimum2
Maximum39320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2025-11-29T20:42:17.925547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile620.95
Q11447.75
median2127
Q33148
95-th percentile6213.2
Maximum39320
Range39318
Interquartile range (IQR)1700.25

Descriptive statistics

Standard deviation2181.6153
Coefficient of variation (CV)0.82769778
Kurtosis32.630927
Mean2635.7631
Median Absolute Deviation (MAD)797
Skewness4.1473435
Sum54402150
Variance4759445.1
MonotonicityNot monotonic
2025-11-29T20:42:18.007806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152718
 
0.1%
158217
 
0.1%
161317
 
0.1%
212716
 
0.1%
160715
 
0.1%
170315
 
0.1%
171715
 
0.1%
147115
 
0.1%
205315
 
0.1%
172215
 
0.1%
Other values (5916)20482
99.2%
ValueCountFrequency (%)
21
 
< 0.1%
61
 
< 0.1%
81
 
< 0.1%
111
 
< 0.1%
121
 
< 0.1%
152
< 0.1%
161
 
< 0.1%
184
< 0.1%
192
< 0.1%
202
< 0.1%
ValueCountFrequency (%)
393201
< 0.1%
379371
< 0.1%
326271
< 0.1%
320541
< 0.1%
304501
< 0.1%
304051
< 0.1%
304011
< 0.1%
282581
< 0.1%
278701
< 0.1%
277001
< 0.1%

total_bedrooms
Real number (ℝ)

High correlation  Missing 

Distinct1923
Distinct (%)9.4%
Missing207
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean537.87055
Minimum1
Maximum6445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2025-11-29T20:42:18.092095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile137
Q1296
median435
Q3647
95-th percentile1275.4
Maximum6445
Range6444
Interquartile range (IQR)351

Descriptive statistics

Standard deviation421.38507
Coefficient of variation (CV)0.78343213
Kurtosis21.985575
Mean537.87055
Median Absolute Deviation (MAD)162
Skewness3.4595463
Sum10990309
Variance177565.38
MonotonicityNot monotonic
2025-11-29T20:42:18.181149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28055
 
0.3%
33151
 
0.2%
34550
 
0.2%
34349
 
0.2%
39349
 
0.2%
32848
 
0.2%
34848
 
0.2%
39448
 
0.2%
27247
 
0.2%
30947
 
0.2%
Other values (1913)19941
96.6%
(Missing)207
 
1.0%
ValueCountFrequency (%)
11
 
< 0.1%
22
 
< 0.1%
35
< 0.1%
47
< 0.1%
56
< 0.1%
65
< 0.1%
76
< 0.1%
88
< 0.1%
97
< 0.1%
108
< 0.1%
ValueCountFrequency (%)
64451
< 0.1%
62101
< 0.1%
54711
< 0.1%
54191
< 0.1%
52901
< 0.1%
50331
< 0.1%
50271
< 0.1%
49571
< 0.1%
49521
< 0.1%
48191
< 0.1%

population
Real number (ℝ)

High correlation 

Distinct3888
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1425.4767
Minimum3
Maximum35682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2025-11-29T20:42:18.261253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile348
Q1787
median1166
Q31725
95-th percentile3288
Maximum35682
Range35679
Interquartile range (IQR)938

Descriptive statistics

Standard deviation1132.4621
Coefficient of variation (CV)0.79444447
Kurtosis73.553116
Mean1425.4767
Median Absolute Deviation (MAD)440
Skewness4.9358582
Sum29421840
Variance1282470.5
MonotonicityNot monotonic
2025-11-29T20:42:18.339616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89125
 
0.1%
85024
 
0.1%
76124
 
0.1%
105224
 
0.1%
122724
 
0.1%
82523
 
0.1%
78222
 
0.1%
99922
 
0.1%
100522
 
0.1%
78121
 
0.1%
Other values (3878)20409
98.9%
ValueCountFrequency (%)
31
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
84
< 0.1%
92
< 0.1%
111
 
< 0.1%
134
< 0.1%
143
< 0.1%
152
< 0.1%
172
< 0.1%
ValueCountFrequency (%)
356821
< 0.1%
285661
< 0.1%
163051
< 0.1%
161221
< 0.1%
155071
< 0.1%
150371
< 0.1%
132511
< 0.1%
128731
< 0.1%
124271
< 0.1%
122031
< 0.1%

households
Real number (ℝ)

High correlation 

Distinct1815
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.53968
Minimum1
Maximum6082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2025-11-29T20:42:18.417387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile125
Q1280
median409
Q3605
95-th percentile1162
Maximum6082
Range6081
Interquartile range (IQR)325

Descriptive statistics

Standard deviation382.32975
Coefficient of variation (CV)0.76536413
Kurtosis22.057988
Mean499.53968
Median Absolute Deviation (MAD)151
Skewness3.4104377
Sum10310499
Variance146176.04
MonotonicityNot monotonic
2025-11-29T20:42:18.499790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30657
 
0.3%
38656
 
0.3%
33556
 
0.3%
28255
 
0.3%
42954
 
0.3%
37553
 
0.3%
29751
 
0.2%
28451
 
0.2%
27850
 
0.2%
34050
 
0.2%
Other values (1805)20107
97.4%
ValueCountFrequency (%)
11
 
< 0.1%
23
 
< 0.1%
34
 
< 0.1%
44
 
< 0.1%
57
< 0.1%
65
< 0.1%
710
< 0.1%
88
< 0.1%
99
< 0.1%
107
< 0.1%
ValueCountFrequency (%)
60821
< 0.1%
53581
< 0.1%
51891
< 0.1%
50501
< 0.1%
49301
< 0.1%
48551
< 0.1%
47691
< 0.1%
46161
< 0.1%
44901
< 0.1%
43721
< 0.1%

median_income
Real number (ℝ)

High correlation 

Distinct12928
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.870671
Minimum0.4999
Maximum15.0001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2025-11-29T20:42:18.579203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.4999
5-th percentile1.60057
Q12.5634
median3.5348
Q34.74325
95-th percentile7.300305
Maximum15.0001
Range14.5002
Interquartile range (IQR)2.17985

Descriptive statistics

Standard deviation1.8998217
Coefficient of variation (CV)0.4908249
Kurtosis4.9525241
Mean3.870671
Median Absolute Deviation (MAD)1.0642
Skewness1.6466567
Sum79890.649
Variance3.6093226
MonotonicityNot monotonic
2025-11-29T20:42:18.659464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.12549
 
0.2%
15.000149
 
0.2%
2.87546
 
0.2%
4.12544
 
0.2%
2.62544
 
0.2%
3.87541
 
0.2%
3.37538
 
0.2%
338
 
0.2%
437
 
0.2%
3.62537
 
0.2%
Other values (12918)20217
98.0%
ValueCountFrequency (%)
0.499912
0.1%
0.53610
< 0.1%
0.54951
 
< 0.1%
0.64331
 
< 0.1%
0.67751
 
< 0.1%
0.68251
 
< 0.1%
0.68311
 
< 0.1%
0.6961
 
< 0.1%
0.69911
 
< 0.1%
0.70071
 
< 0.1%
ValueCountFrequency (%)
15.000149
0.2%
152
 
< 0.1%
14.90091
 
< 0.1%
14.58331
 
< 0.1%
14.42191
 
< 0.1%
14.41131
 
< 0.1%
14.29591
 
< 0.1%
14.28671
 
< 0.1%
13.9471
 
< 0.1%
13.85561
 
< 0.1%

median_house_value
Real number (ℝ)

High correlation 

Distinct3842
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206855.82
Minimum14999
Maximum500001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2025-11-29T20:42:18.742135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14999
5-th percentile66200
Q1119600
median179700
Q3264725
95-th percentile489810
Maximum500001
Range485002
Interquartile range (IQR)145125

Descriptive statistics

Standard deviation115395.62
Coefficient of variation (CV)0.55785531
Kurtosis0.32787024
Mean206855.82
Median Absolute Deviation (MAD)68400
Skewness0.97776327
Sum4.2695041 × 109
Variance1.3316148 × 1010
MonotonicityNot monotonic
2025-11-29T20:42:18.835157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500001965
 
4.7%
137500122
 
0.6%
162500117
 
0.6%
112500103
 
0.5%
18750093
 
0.5%
22500092
 
0.4%
35000079
 
0.4%
8750078
 
0.4%
27500065
 
0.3%
15000064
 
0.3%
Other values (3832)18862
91.4%
ValueCountFrequency (%)
149994
< 0.1%
175001
 
< 0.1%
225004
< 0.1%
250001
 
< 0.1%
266001
 
< 0.1%
269001
 
< 0.1%
275001
 
< 0.1%
283001
 
< 0.1%
300002
< 0.1%
325004
< 0.1%
ValueCountFrequency (%)
500001965
4.7%
50000027
 
0.1%
4991001
 
< 0.1%
4990001
 
< 0.1%
4988001
 
< 0.1%
4987001
 
< 0.1%
4986001
 
< 0.1%
4984001
 
< 0.1%
4976001
 
< 0.1%
4974001
 
< 0.1%

ocean_proximity
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.4 KiB
<1H OCEAN
9136 
INLAND
6551 
NEAR OCEAN
2658 
NEAR BAY
2290 
ISLAND
 
5

Length

Max length10
Median length9
Mean length8.0649225
Min length6

Characters and Unicode

Total characters166460
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNEAR BAY
2nd rowNEAR BAY
3rd rowNEAR BAY
4th rowNEAR BAY
5th rowNEAR BAY

Common Values

ValueCountFrequency (%)
<1H OCEAN9136
44.3%
INLAND6551
31.7%
NEAR OCEAN2658
 
12.9%
NEAR BAY2290
 
11.1%
ISLAND5
 
< 0.1%

Length

2025-11-29T20:42:18.915693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T20:42:18.984956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ocean11794
34.0%
1h9136
26.3%
inland6551
18.9%
near4948
14.2%
bay2290
 
6.6%
island5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N29849
17.9%
A25588
15.4%
E16742
10.1%
14084
8.5%
O11794
 
7.1%
C11794
 
7.1%
<9136
 
5.5%
H9136
 
5.5%
19136
 
5.5%
I6556
 
3.9%
Other values (6)22645
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)166460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N29849
17.9%
A25588
15.4%
E16742
10.1%
14084
8.5%
O11794
 
7.1%
C11794
 
7.1%
<9136
 
5.5%
H9136
 
5.5%
19136
 
5.5%
I6556
 
3.9%
Other values (6)22645
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)166460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N29849
17.9%
A25588
15.4%
E16742
10.1%
14084
8.5%
O11794
 
7.1%
C11794
 
7.1%
<9136
 
5.5%
H9136
 
5.5%
19136
 
5.5%
I6556
 
3.9%
Other values (6)22645
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)166460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N29849
17.9%
A25588
15.4%
E16742
10.1%
14084
8.5%
O11794
 
7.1%
C11794
 
7.1%
<9136
 
5.5%
H9136
 
5.5%
19136
 
5.5%
I6556
 
3.9%
Other values (6)22645
13.6%

Interactions

2025-11-29T20:42:16.235436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:06.257782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:07.414079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:08.506740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:09.658128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:11.118514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:12.740411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:14.300914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:15.442921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:16.301960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:06.408174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:07.553681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:08.598612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:09.800417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:11.293227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:12.890825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:14.439183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:15.531079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:16.372261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:06.561769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:07.656885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:08.724117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:09.939814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:11.422674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:13.109296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:14.553264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:15.607484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:16.437488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:06.719611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:07.759354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:08.839687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:10.077155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:11.559597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:13.296047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:14.661863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:15.704731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:16.509047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:06.865026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:07.956573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:08.954376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:10.287426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:11.776678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:13.429593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:14.756327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:15.805832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:16.577316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:06.974617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:08.065843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:09.074552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:10.501782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:11.973772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:13.687968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:14.857069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:15.904856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:16.646641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:07.096588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:08.176037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:09.193184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:10.661696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:12.176470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:13.931873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:15.035280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:16.002257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:16.714216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:07.212759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:08.270273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:09.313371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:10.790351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:12.368109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:14.066589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:15.206791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:16.090156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:16.783909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:07.310615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:08.392882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:09.510522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:10.951395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:12.524936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:14.183453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:15.339238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T20:42:16.167221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-29T20:42:19.046295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
householdshousing_median_agelatitudelongitudemedian_house_valuemedian_incomeocean_proximitypopulationtotal_bedroomstotal_rooms
households1.000-0.282-0.0740.0600.1130.0300.0190.9040.9760.907
housing_median_age-0.2821.0000.032-0.1510.075-0.1470.190-0.284-0.307-0.357
latitude-0.0740.0321.000-0.879-0.166-0.0880.470-0.124-0.057-0.018
longitude0.060-0.151-0.8791.000-0.070-0.0100.4250.1240.0640.040
median_house_value0.1130.075-0.166-0.0701.0000.6770.3020.0040.0860.206
median_income0.030-0.147-0.088-0.0100.6771.0000.1250.006-0.0060.271
ocean_proximity0.0190.1900.4700.4250.3020.1251.0000.0140.0170.021
population0.904-0.284-0.1240.1240.0040.0060.0141.0000.8710.816
total_bedrooms0.976-0.307-0.0570.0640.086-0.0060.0170.8711.0000.915
total_rooms0.907-0.357-0.0180.0400.2060.2710.0210.8160.9151.000

Missing values

2025-11-29T20:42:16.888662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-29T20:42:16.983288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
0-122.2337.8841.0880.0129.0322.0126.08.3252452600.0NEAR BAY
1-122.2237.8621.07099.01106.02401.01138.08.3014358500.0NEAR BAY
2-122.2437.8552.01467.0190.0496.0177.07.2574352100.0NEAR BAY
3-122.2537.8552.01274.0235.0558.0219.05.6431341300.0NEAR BAY
4-122.2537.8552.01627.0280.0565.0259.03.8462342200.0NEAR BAY
5-122.2537.8552.0919.0213.0413.0193.04.0368269700.0NEAR BAY
6-122.2537.8452.02535.0489.01094.0514.03.6591299200.0NEAR BAY
7-122.2537.8452.03104.0687.01157.0647.03.1200241400.0NEAR BAY
8-122.2637.8442.02555.0665.01206.0595.02.0804226700.0NEAR BAY
9-122.2537.8452.03549.0707.01551.0714.03.6912261100.0NEAR BAY
longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
20630-121.3239.2911.02640.0505.01257.0445.03.5673112000.0INLAND
20631-121.4039.3315.02655.0493.01200.0432.03.5179107200.0INLAND
20632-121.4539.2615.02319.0416.01047.0385.03.1250115600.0INLAND
20633-121.5339.1927.02080.0412.01082.0382.02.549598300.0INLAND
20634-121.5639.2728.02332.0395.01041.0344.03.7125116800.0INLAND
20635-121.0939.4825.01665.0374.0845.0330.01.560378100.0INLAND
20636-121.2139.4918.0697.0150.0356.0114.02.556877100.0INLAND
20637-121.2239.4317.02254.0485.01007.0433.01.700092300.0INLAND
20638-121.3239.4318.01860.0409.0741.0349.01.867284700.0INLAND
20639-121.2439.3716.02785.0616.01387.0530.02.388689400.0INLAND